Titlebook: Intelligent Systems and Pattern Recognition; Second International Akram Bennour,Tolga Ensari,Sean Eom Conference proceedings 2022 Springer

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书目名称Intelligent Systems and Pattern Recognition影响因子(影响力)




书目名称Intelligent Systems and Pattern Recognition影响因子(影响力)学科排名




书目名称Intelligent Systems and Pattern Recognition网络公开度




书目名称Intelligent Systems and Pattern Recognition网络公开度学科排名




书目名称Intelligent Systems and Pattern Recognition被引频次




书目名称Intelligent Systems and Pattern Recognition被引频次学科排名




书目名称Intelligent Systems and Pattern Recognition年度引用




书目名称Intelligent Systems and Pattern Recognition年度引用学科排名




书目名称Intelligent Systems and Pattern Recognition读者反馈




书目名称Intelligent Systems and Pattern Recognition读者反馈学科排名

爱国者

杂役

A New Study of Needs and Motivations Generated by Virtual Reality Games and Factor Products for Geneotivation for virtual reality games and the factor products via correlational research. The target group is framed from the demographic data in Bangkok in 2019 by the National Statistical Office of Thailand. 40 people around 18–24 years old are selected. We then develop new virtual reality games fro

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孤僻

Improved Cerebral Images Semantic Segmentation Using Advanced Approaches of Deep Learningdetection (CADe) systems can reduce the workload of physicians and minimize the time required for accurate segmentation of illnesses. CADe systems for brain tumors comprises two principles stages: pre-processing of MRI images, and segmentation to define the region of interest (ROI). This paper descr

拥护者

Self-supervised Learning for COVID-19 Detection from Chest X-ray Images explosion of massive sets of unlabeled data. In the field of medical imaging for example, creating labels is extremely time-consuming because professionals should spend countless hours looking at images to manually annotate, segment, etc. Recently, several works are looking for solutions to the cha

Fillet,Filet

Deep Learning-Based Segmentation of Connected Components in Arabic Handwritten Documentsn Arabic manuscripts to be segmented correctly. It is the first deep learning-based method proposed to solve this problem. It is based on a modified U-Net named AR2U-net: an Attention-based Recurrent Residual U-net model trained to separate touching characters. It is trained on the LTP (Local Touchi

Dendritic-Cells

Classifying the Human Activities of Sensor Data Using Deep Neural Network fast data access, huge volume, as well as the most prominent feature, the concept drift. Machine learning in general and deep learning technique in particular is among the predominant and successful selections to classify the human activities. This is due to several reasons such as results quality

ARENA